Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Adds real context beyond the annotations: the pricing model ($0.002/call via x402 USDC or card key), that failed calls are free, and it explicitly reconciles the readOnlyHint=false annotation ('Reads or computes only; not marked read-only because each call is charged'). This explains a behavioral trait an agent would otherwise find confusing. It does not cover rate limits or return shape, so not a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, front-loaded with the output description, then use cases, then behavior/pricing. Every sentence carries information; the pricing clause is dense but justifies its place by explaining the readOnlyHint=false.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 2-parameter tool with no output schema, the description covers the output nature (ordered keywords), use cases, and the cost/behavior profile. It is nearly complete; only explicit sibling routing and return format detail are absent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both 'text' (12,000 char limit) and 'count' (3-30, default 10). The description adds the output ordering ('most relevant first') but no parameter-specific meaning beyond what the schema provides, matching the baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource and output: 'The most important keywords and key phrases of a text, most relevant first,' which clearly distinguishes keyword extraction from siblings like ai_summarize_text or ai_classify. It does not explicitly name an alternative sibling, but the resource itself is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete use cases ('tagging, SEO, search indexing and topic detection') that imply when to reach for this tool over siblings. It stops short of naming a specific alternative or an exclusion condition, so it is clear context but not full when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.